---
title: "Dashboard"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    source_code: embed
---

```{r setup, include=FALSE}

# rmarkdown::render("dashboard.Rmd", output_format = "flexdashboard::flex_dashboard")

library(flexdashboard)
library(tidyverse)
library(p8105.datasets)

library(plotly)

library(patchwork)


theme_set(theme_minimal() + theme(legend.position = 'bottom'))

options(
   ggplot2.continuous.colour = 'viridis',
   ggplot2.continuous.fill = 'viridis'
 )

scale_colour_discrete = scale_colour_viridis_d
scale_fill_discrete = scale_fill_viridis_d
# Set a seed
set.seed(777)
```

```{r Prepare data, echo = FALSE, warning = FALSE, message = FALSE}

data(ny_noaa)
# data(nyc_airbnb)

year_filter = '2017'


ny_noaa = 
  ny_noaa %>% 
  janitor::clean_names() %>% 
  # Pick 4 stations with few missing values
  filter(
    id %in% c('USC00300055', 'USC00301401', 'USC00303025')
  ) %>%  
  drop_na() %>% 
  mutate(tmax = as.double(tmax)/10,
         tmin = as.double(tmin)/10) %>% 
  rename(station_id = id)
  
# ny_noaa


```

Column {data-width=650}
-----------------------------------------------------------------------

### Chart A

```{r Scores by month and cuisine_description, echo = FALSE, warning = FALSE, message = FALSE}
mean_tmin_df = 
  ny_noaa %>% 
  mutate(year = lubridate::year(date)) %>% 
  group_by(year, station_id) %>% 
  summarise(mean_tmin = mean(tmin))
  # plot_ly(
  #   x = ~year, y = ~mean_tmin, type = "scatter", mode = "markers",
  #   color = ~station_id) +
  # theme(axis.text.x = element_text(angle = 90, hjust = 1))
Boxplot = 
  ggplot(mean_tmin_df, aes(x = year, y = mean_tmin, color = station_id)) + 
  geom_line()

ggplotly(Boxplot)
```

Column {data-width=350}
-----------------------------------------------------------------------

### Chart B

```{r, echo = FALSE, warning = FALSE, message = FALSE}

ny_noaa %>% 
  mutate(month = lubridate::month(date),
         year = lubridate::year(date),
         season = case_when(
           month > 2 & month <= 5 ~ 'Spring',
           month > 5 & month <= 8 ~ 'Summer',
           month > 8 & month <= 11 ~ 'Autumn',
           TRUE ~ 'Winter'
         ),
         season = fct_reorder(season, month, max),
         temperature_diff = tmax - tmin) %>% 
  filter(year >= 2000) %>% 
  
  plot_ly(y = ~temperature_diff, color = ~season, type = "box", colors = "viridis")

```

### Chart C

```{r, echo = FALSE, warning = FALSE, message = FALSE}

ny_noaa %>% 
  mutate(month = lubridate::month(date),
         year = lubridate::year(date)) %>% 
  filter(year >= 2000) %>% 
  group_by(month) %>% 
  summarise(mean_prcp = mean(prcp),
            mean_tmin = mean(tmin)) %>% 
  plot_ly(x = ~month, y = ~mean_prcp, color = ~mean_tmin, type = "bar", colors = "Blues")


```